Machine Learning Operations Engineer II
About The Position
The Machine Learning Operations Engineer supports the platforms, tools, and processes that move models from development into production and serve predictions in real time. The role partners with Data Science, Data Product Management, Product Engineering, and Data Platform teams to create and support tools and processes that automate model productionalization. The team develops cloud-native capabilities to help deliver predictive modeling solutions more quickly, accurately, and efficiently, including solutions that help prevent fraud and protect the banking system. The position designs, builds, and maintains scalable machine-learning infrastructure and pipelines; improves the orchestration and reliability of model workflows; and monitors infrastructure and production model performance. It also develops tools for experimentation, data analysis, model versioning, and artifact management, supports data and model governance, and provides technical assistance to team members. The role contributes to the company’s risk-management commitment and protection of systems and data.
Requirements
- Bachelor’s degree in Computer Science, Engineering, or a related field, or education and experience typically obtained through its completion.
- Typically at least 2 years of experience or an internship in Data Science, ML Engineering, or an ML Ops capacity.
- Intermediate programming skills in Python and experience with data science and machine-learning packages and frameworks.
- Demonstrated experience with AWS services.
- Intermediate proficiency with containerization technologies such as Docker and Kubernetes, and CI/CD practices.
- Demonstrated experience with MLOps tools such as MLflow, Kubeflow, or similar platforms.
- Understanding and application of data management, distributed computing, and software architecture principles.
- Proven experience delivering real-time models in production environments.
- Must pass a background and drug screen.
- Must independently be eligible to work in the United States for any employer at the date of hire; the position is not eligible for employment visa sponsorship.
Nice To Haves
- Additional related education and/or work experience.
- Experience in hybrid on-premises/cloud environments.
- Hadoop, Hive, or Cloudera experience.
- Distributed-computing programming skills such as Spark.
- Experience with Scala or Java.
Responsibilities
- Design, build, and maintain scalable ML infrastructure and pipelines for model training, deployment, and monitoring.
- Optimize orchestration processes for efficient deployment and management of predictive models.
- Monitor and maintain the performance, security, and scalability of ML infrastructure.
- Collaborate with data scientists and software engineers to streamline the ML lifecycle from development to production.
- Develop and maintain tools for data analysis, experimentation, model versioning, and artifact management; support data and model governance requirements as needed.
- Create monitoring systems to measure and trend model performance, detect model drift, and ensure production models perform optimally.
- Develop automation scripts and tools to improve the efficiency and reliability of MLOps processes.
- Optimize ML workflows for efficiency, scalability, and reliability.
- Provide technical assistance to team members, troubleshoot moderately complex issues, and escalate issues as necessary.
- Support the company’s risk-management commitment and protect the integrity and confidentiality of systems and data.
- Perform other related duties as assigned by the supervisor.
Benefits
- Competitive medical plans (PPO/HDHP), dental insurance, and vision insurance.
- Company contributions to a Health Savings Account (HSA) or pre-tax flexible spending accounts for commuting, health, and dependent-care expenses.
- 401(k) retirement plan with a 100% company Safe Harbor match on the first 6% deferred, immediately upon eligibility.
- Flexible Time Off for exempt employees and generous PTO for non-exempt employees.
- 11 paid company holidays and a paid volunteer day.
- 12 weeks of paid parental leave.
- Maven Family Planning support, including egg freezing, fertility, adoption, surrogacy, pregnancy, postpartum, early pediatrics, and returning to work.
- Discretionary incentive plan.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
